Overview

Brought to you by YData

Dataset statistics

Number of variables46
Number of observations43013
Missing cells241788
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.1 MiB
Average record size in memory1.8 KiB

Variable types

Text10
Categorical18
Numeric12
DateTime2
URL1
Unsupported2
Boolean1

Alerts

chimenea has constant value "0.0"Constant
permite_mascotas has constant value "0.0"Constant
salon_comunal has constant value "0.0"Constant
administracion is highly overall correlated with area and 6 other fieldsHigh correlation
antiguedad is highly overall correlated with estadoHigh correlation
area is highly overall correlated with administracion and 6 other fieldsHigh correlation
banos is highly overall correlated with administracion and 6 other fieldsHigh correlation
distancia_estacion_tm_m is highly overall correlated with is_cerca_estacion_tmHigh correlation
distancia_parque_m is highly overall correlated with is_cerca_parqueHigh correlation
estado is highly overall correlated with administracion and 5 other fieldsHigh correlation
estrato is highly overall correlated with administracion and 6 other fieldsHigh correlation
habitaciones is highly overall correlated with area and 1 other fieldsHigh correlation
is_cerca_estacion_tm is highly overall correlated with distancia_estacion_tm_mHigh correlation
is_cerca_parque is highly overall correlated with distancia_parque_mHigh correlation
latitud is highly overall correlated with localidadHigh correlation
localidad is highly overall correlated with latitud and 1 other fieldsHigh correlation
longitud is highly overall correlated with estrato and 1 other fieldsHigh correlation
parqueaderos is highly overall correlated with administracion and 5 other fieldsHigh correlation
precio_arriendo is highly overall correlated with administracion and 9 other fieldsHigh correlation
precio_venta is highly overall correlated with administracion and 5 other fieldsHigh correlation
terraza is highly overall correlated with estado and 1 other fieldsHigh correlation
tipo_propiedad is highly overall correlated with estado and 1 other fieldsHigh correlation
website is highly overall correlated with estado and 1 other fieldsHigh correlation
tipo_propiedad is highly imbalanced (98.8%)Imbalance
website is highly imbalanced (86.7%)Imbalance
estado is highly imbalanced (95.2%)Imbalance
jacuzzi is highly imbalanced (73.4%)Imbalance
piscina is highly imbalanced (56.2%)Imbalance
terraza is highly imbalanced (98.8%)Imbalance
coords_modified is highly imbalanced (85.9%)Imbalance
precio_venta has 15429 (35.9%) missing valuesMissing
administracion has 7925 (18.4%) missing valuesMissing
sector has 1641 (3.8%) missing valuesMissing
direccion has 42219 (98.2%) missing valuesMissing
url has 42219 (98.2%) missing valuesMissing
timeline has 13586 (31.6%) missing valuesMissing
estado has 796 (1.9%) missing valuesMissing
compañia has 4530 (10.5%) missing valuesMissing
precio_arriendo has 27184 (63.2%) missing valuesMissing
piso has 43013 (100.0%) missing valuesMissing
closets has 43013 (100.0%) missing valuesMissing
precio_venta is highly skewed (γ1 = 52.77336024)Skewed
area is highly skewed (γ1 = 207.2315068)Skewed
administracion is highly skewed (γ1 = 28.87197514)Skewed
precio_arriendo is highly skewed (γ1 = 48.00865601)Skewed
_id has unique valuesUnique
codigo has unique valuesUnique
piso is an unsupported type, check if it needs cleaning or further analysisUnsupported
closets is an unsupported type, check if it needs cleaning or further analysisUnsupported
parqueaderos has 6615 (15.4%) zerosZeros

Reproduction

Analysis started2025-11-23 17:45:26.576943
Analysis finished2025-11-23 17:45:41.621581
Duration15.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

_id
Text

Unique 

Distinct43013
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2025-11-23T12:45:41.692209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters1032312
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43013 ?
Unique (%)100.0%

Sample

1st row66d86c7eceda690e85508760
2nd row66d86c7eceda690e85508761
3rd row66d86c7eceda690e85508762
4th row66d86c7eceda690e85508763
5th row66d86c7eceda690e85508764
ValueCountFrequency (%)
66d86c7eceda690e855087651
 
< 0.1%
66d86ebfceda690e85512f641
 
< 0.1%
66d86c7eceda690e855087601
 
< 0.1%
66d86c7eceda690e855087611
 
< 0.1%
66d86c7eceda690e855087621
 
< 0.1%
66d86ebfceda690e85512f551
 
< 0.1%
66d86ebfceda690e85512f561
 
< 0.1%
66d86ebfceda690e85512f571
 
< 0.1%
66d86ebfceda690e85512f581
 
< 0.1%
66d86ebfceda690e85512f591
 
< 0.1%
Other values (43003)43003
> 99.9%
2025-11-23T12:45:41.794887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6184438
17.9%
d119686
11.6%
e112084
10.9%
8104777
10.1%
598464
9.5%
090886
8.8%
c78458
7.6%
962383
 
6.0%
a61822
 
6.0%
128992
 
2.8%
Other values (6)90322
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1032312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6184438
17.9%
d119686
11.6%
e112084
10.9%
8104777
10.1%
598464
9.5%
090886
8.8%
c78458
7.6%
962383
 
6.0%
a61822
 
6.0%
128992
 
2.8%
Other values (6)90322
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1032312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6184438
17.9%
d119686
11.6%
e112084
10.9%
8104777
10.1%
598464
9.5%
090886
8.8%
c78458
7.6%
962383
 
6.0%
a61822
 
6.0%
128992
 
2.8%
Other values (6)90322
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1032312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6184438
17.9%
d119686
11.6%
e112084
10.9%
8104777
10.1%
598464
9.5%
090886
8.8%
c78458
7.6%
962383
 
6.0%
a61822
 
6.0%
128992
 
2.8%
Other values (6)90322
8.7%

codigo
Text

Unique 

Distinct43013
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2025-11-23T12:45:41.896938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.358124
Min length6

Characters and Unicode

Total characters531560
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43013 ?
Unique (%)100.0%

Sample

1st row4133491
2nd row3889852
3rd row4386770
4th row4210060
5th row4063762
ValueCountFrequency (%)
40066191
 
< 0.1%
mc47219771
 
< 0.1%
41334911
 
< 0.1%
38898521
 
< 0.1%
43867701
 
< 0.1%
44819671
 
< 0.1%
43076591
 
< 0.1%
43429951
 
< 0.1%
40021101
 
< 0.1%
44230151
 
< 0.1%
Other values (43003)43003
> 99.9%
2025-11-23T12:45:42.036823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
461440
11.6%
155534
10.4%
550288
9.5%
044293
8.3%
343519
8.2%
242235
7.9%
M40753
7.7%
-38711
7.3%
938598
7.3%
738145
7.2%
Other values (9)78044
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)531560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
461440
11.6%
155534
10.4%
550288
9.5%
044293
8.3%
343519
8.2%
242235
7.9%
M40753
7.7%
-38711
7.3%
938598
7.3%
738145
7.2%
Other values (9)78044
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)531560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
461440
11.6%
155534
10.4%
550288
9.5%
044293
8.3%
343519
8.2%
242235
7.9%
M40753
7.7%
-38711
7.3%
938598
7.3%
738145
7.2%
Other values (9)78044
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)531560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
461440
11.6%
155534
10.4%
550288
9.5%
044293
8.3%
343519
8.2%
242235
7.9%
M40753
7.7%
-38711
7.3%
938598
7.3%
738145
7.2%
Other values (9)78044
14.7%

tipo_propiedad
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
APARTAMENTO
42943 
CASA CON CONJUNTO CERRADO
 
60
CASA
 
10

Length

Max length25
Median length11
Mean length11.017902
Min length4

Characters and Unicode

Total characters473913
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPARTAMENTO
2nd rowAPARTAMENTO
3rd rowAPARTAMENTO
4th rowAPARTAMENTO
5th rowAPARTAMENTO

Common Values

ValueCountFrequency (%)
APARTAMENTO42943
99.8%
CASA CON CONJUNTO CERRADO60
 
0.1%
CASA10
 
< 0.1%

Length

2025-11-23T12:45:42.081004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:42.110816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
apartamento42943
99.4%
casa70
 
0.2%
con60
 
0.1%
conjunto60
 
0.1%
cerrado60
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A129029
27.2%
T85946
18.1%
O43183
 
9.1%
N43123
 
9.1%
R43063
 
9.1%
E43003
 
9.1%
M42943
 
9.1%
P42943
 
9.1%
C250
 
0.1%
180
 
< 0.1%
Other values (4)250
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)473913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A129029
27.2%
T85946
18.1%
O43183
 
9.1%
N43123
 
9.1%
R43063
 
9.1%
E43003
 
9.1%
M42943
 
9.1%
P42943
 
9.1%
C250
 
0.1%
180
 
< 0.1%
Other values (4)250
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)473913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A129029
27.2%
T85946
18.1%
O43183
 
9.1%
N43123
 
9.1%
R43063
 
9.1%
E43003
 
9.1%
M42943
 
9.1%
P42943
 
9.1%
C250
 
0.1%
180
 
< 0.1%
Other values (4)250
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)473913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A129029
27.2%
T85946
18.1%
O43183
 
9.1%
N43123
 
9.1%
R43063
 
9.1%
E43003
 
9.1%
M42943
 
9.1%
P42943
 
9.1%
C250
 
0.1%
180
 
< 0.1%
Other values (4)250
 
0.1%

tipo_operacion
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
VENTA
27270 
ARRIENDO
15515 
VENTA Y ARRIENDO
 
228

Length

Max length16
Median length5
Mean length6.1404227
Min length5

Characters and Unicode

Total characters264118
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVENTA
2nd rowVENTA
3rd rowVENTA
4th rowVENTA
5th rowVENTA

Common Values

ValueCountFrequency (%)
VENTA27270
63.4%
ARRIENDO15515
36.1%
VENTA Y ARRIENDO228
 
0.5%

Length

2025-11-23T12:45:42.144089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:42.169850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
venta27498
63.3%
arriendo15743
36.2%
y228
 
0.5%

Most occurring characters

ValueCountFrequency (%)
E43241
16.4%
N43241
16.4%
A43241
16.4%
R31486
11.9%
V27498
10.4%
T27498
10.4%
I15743
 
6.0%
D15743
 
6.0%
O15743
 
6.0%
456
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)264118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E43241
16.4%
N43241
16.4%
A43241
16.4%
R31486
11.9%
V27498
10.4%
T27498
10.4%
I15743
 
6.0%
D15743
 
6.0%
O15743
 
6.0%
456
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)264118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E43241
16.4%
N43241
16.4%
A43241
16.4%
R31486
11.9%
V27498
10.4%
T27498
10.4%
I15743
 
6.0%
D15743
 
6.0%
O15743
 
6.0%
456
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)264118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E43241
16.4%
N43241
16.4%
A43241
16.4%
R31486
11.9%
V27498
10.4%
T27498
10.4%
I15743
 
6.0%
D15743
 
6.0%
O15743
 
6.0%
456
 
0.2%

precio_venta
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct2808
Distinct (%)10.2%
Missing15429
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean2.3647941 × 109
Minimum1000000
Maximum4.25 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:42.208899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1.84 × 108
Q14.029 × 108
median7 × 108
Q31.33 × 109
95-th percentile3.1 × 109
Maximum4.25 × 1012
Range4.249999 × 1012
Interquartile range (IQR)9.271 × 108

Descriptive statistics

Standard deviation5.5741976 × 1010
Coefficient of variation (CV)23.571598
Kurtosis3085.0787
Mean2.3647941 × 109
Median Absolute Deviation (MAD)3.8 × 108
Skewness52.77336
Sum6.5230482 × 1013
Variance3.1071679 × 1021
MonotonicityNot monotonic
2025-11-23T12:45:42.258479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000000402
 
0.9%
1100000000368
 
0.9%
650000000348
 
0.8%
1300000000345
 
0.8%
750000000337
 
0.8%
850000000331
 
0.8%
450000000323
 
0.8%
550000000322
 
0.7%
1400000000265
 
0.6%
1500000000247
 
0.6%
Other values (2798)24296
56.5%
(Missing)15429
35.9%
ValueCountFrequency (%)
10000002
 
< 0.1%
11000006
< 0.1%
11300001
 
< 0.1%
11500001
 
< 0.1%
11600001
 
< 0.1%
11950001
 
< 0.1%
12000002
 
< 0.1%
12500001
 
< 0.1%
12800001
 
< 0.1%
13000002
 
< 0.1%
ValueCountFrequency (%)
4.25 × 10121
< 0.1%
3.3 × 10121
< 0.1%
3.24 × 10121
< 0.1%
3 × 10121
< 0.1%
2.9 × 10121
< 0.1%
2.8 × 10121
< 0.1%
2.1 × 10121
< 0.1%
1.98 × 10121
< 0.1%
1.8 × 10121
< 0.1%
1.45 × 10121
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct4494
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.03406
Minimum0
Maximum1900000
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:42.304858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q163
median100
Q3174
95-th percentile323.872
Maximum1900000
Range1900000
Interquartile range (IQR)111

Descriptive statistics

Standard deviation9163.0016
Coefficient of variation (CV)50.895934
Kurtosis42967.43
Mean180.03406
Median Absolute Deviation (MAD)46
Skewness207.23151
Sum7743805
Variance83960598
MonotonicityNot monotonic
2025-11-23T12:45:42.356554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60736
 
1.7%
50627
 
1.5%
90614
 
1.4%
70609
 
1.4%
80582
 
1.4%
45485
 
1.1%
120432
 
1.0%
40431
 
1.0%
55422
 
1.0%
150389
 
0.9%
Other values (4484)37686
87.6%
ValueCountFrequency (%)
019
< 0.1%
18
< 0.1%
23
 
< 0.1%
4.931
 
< 0.1%
51
 
< 0.1%
5.221
 
< 0.1%
5.431
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
7.21
 
< 0.1%
ValueCountFrequency (%)
19000001
< 0.1%
196211
< 0.1%
154131
< 0.1%
139851
< 0.1%
99991
< 0.1%
92421
< 0.1%
87261
< 0.1%
76851
< 0.1%
72631
< 0.1%
66121
< 0.1%

habitaciones
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.6153399
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:42.394307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85004583
Coefficient of variation (CV)0.32502308
Kurtosis-0.028179367
Mean2.6153399
Median Absolute Deviation (MAD)0
Skewness-0.28029586
Sum112491
Variance0.72257792
MonotonicityNot monotonic
2025-11-23T12:45:42.422417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
322818
53.0%
210625
24.7%
15309
 
12.3%
43825
 
8.9%
5433
 
1.0%
61
 
< 0.1%
71
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
15309
 
12.3%
210625
24.7%
322818
53.0%
43825
 
8.9%
5433
 
1.0%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
61
 
< 0.1%
5433
 
1.0%
43825
 
8.9%
322818
53.0%
210625
24.7%
15309
 
12.3%

banos
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.7013392
Minimum0
Maximum6
Zeros23
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:42.452686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1966586
Coefficient of variation (CV)0.44298719
Kurtosis-0.73107553
Mean2.7013392
Median Absolute Deviation (MAD)1
Skewness0.39683148
Sum116190
Variance1.4319919
MonotonicityNot monotonic
2025-11-23T12:45:42.484981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
214825
34.5%
310605
24.7%
46615
15.4%
16614
15.4%
54329
 
10.1%
023
 
0.1%
61
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
023
 
0.1%
16614
15.4%
214825
34.5%
310605
24.7%
46615
15.4%
54329
 
10.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
54329
 
10.1%
46615
15.4%
310605
24.7%
214825
34.5%
16614
15.4%
023
 
0.1%

administracion
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct4800
Distinct (%)13.7%
Missing7925
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean4059551.3
Minimum1
Maximum3.5 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:42.529997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile105000
Q1349000
median650000
Q31200000
95-th percentile2500000
Maximum3.5 × 109
Range3.5 × 109
Interquartile range (IQR)851000

Descriptive statistics

Standard deviation67816415
Coefficient of variation (CV)16.705397
Kurtosis1032.4606
Mean4059551.3
Median Absolute Deviation (MAD)375000
Skewness28.871975
Sum1.4244154 × 1011
Variance4.5990662 × 1015
MonotonicityNot monotonic
2025-11-23T12:45:42.579749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000632
 
1.5%
1500000560
 
1.3%
500000545
 
1.3%
1200000540
 
1.3%
400000460
 
1.1%
1300000434
 
1.0%
300000424
 
1.0%
600000405
 
0.9%
1100000398
 
0.9%
10000391
 
0.9%
Other values (4790)30299
70.4%
(Missing)7925
 
18.4%
ValueCountFrequency (%)
117
< 0.1%
105
 
< 0.1%
333
 
< 0.1%
1001
 
< 0.1%
1111
 
< 0.1%
1401
 
< 0.1%
1501
 
< 0.1%
1801
 
< 0.1%
2001
 
< 0.1%
2251
 
< 0.1%
ValueCountFrequency (%)
35000000002
< 0.1%
33660000001
 
< 0.1%
29180000001
 
< 0.1%
24000000001
 
< 0.1%
21000000001
 
< 0.1%
20000000003
< 0.1%
19000000001
 
< 0.1%
18900000001
 
< 0.1%
18000000001
 
< 0.1%
17500000002
< 0.1%

parqueaderos
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.6628615
Minimum-2
Maximum30
Zeros6615
Zeros (%)15.4%
Negative1
Negative (%)< 0.1%
Memory size336.2 KiB
2025-11-23T12:45:42.618811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum30
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1146851
Coefficient of variation (CV)0.67034153
Kurtosis12.722587
Mean1.6628615
Median Absolute Deviation (MAD)1
Skewness0.92108285
Sum71523
Variance1.242523
MonotonicityNot monotonic
2025-11-23T12:45:42.650071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
215525
36.1%
112678
29.5%
06615
15.4%
35045
 
11.7%
43143
 
7.3%
102
 
< 0.1%
202
 
< 0.1%
-21
 
< 0.1%
301
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
-21
 
< 0.1%
06615
15.4%
112678
29.5%
215525
36.1%
35045
 
11.7%
43143
 
7.3%
102
 
< 0.1%
202
 
< 0.1%
301
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
202
 
< 0.1%
102
 
< 0.1%
43143
 
7.3%
35045
 
11.7%
215525
36.1%
112678
29.5%
06615
15.4%
-21
 
< 0.1%

sector
Text

Missing 

Distinct103
Distinct (%)0.2%
Missing1641
Missing (%)3.8%
Memory size2.4 MiB
2025-11-23T12:45:42.705784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length26
Mean length11.332471
Min length4

Characters and Unicode

Total characters468847
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowBRITALIA
2nd rowSAN CRISTOBAL NORTE
3rd rowLA SABANA
4th rowTIBABUYES
5th rowVERBENAL
ValueCountFrequency (%)
chico10678
 
14.3%
santa7282
 
9.7%
barbara7279
 
9.7%
y4703
 
6.3%
country3324
 
4.5%
colina2892
 
3.9%
alrededores2892
 
3.9%
de2738
 
3.7%
cedritos2571
 
3.4%
chapinero2168
 
2.9%
Other values (122)28165
37.7%
2025-11-23T12:45:42.812676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A72253
15.4%
R41635
 
8.9%
O41437
 
8.8%
C37654
 
8.0%
33320
 
7.1%
I31113
 
6.6%
N29615
 
6.3%
E29277
 
6.2%
T24791
 
5.3%
S22541
 
4.8%
Other values (18)105211
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)468847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A72253
15.4%
R41635
 
8.9%
O41437
 
8.8%
C37654
 
8.0%
33320
 
7.1%
I31113
 
6.6%
N29615
 
6.3%
E29277
 
6.2%
T24791
 
5.3%
S22541
 
4.8%
Other values (18)105211
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)468847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A72253
15.4%
R41635
 
8.9%
O41437
 
8.8%
C37654
 
8.0%
33320
 
7.1%
I31113
 
6.6%
N29615
 
6.3%
E29277
 
6.2%
T24791
 
5.3%
S22541
 
4.8%
Other values (18)105211
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)468847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A72253
15.4%
R41635
 
8.9%
O41437
 
8.8%
C37654
 
8.0%
33320
 
7.1%
I31113
 
6.6%
N29615
 
6.3%
E29277
 
6.2%
T24791
 
5.3%
S22541
 
4.8%
Other values (18)105211
22.4%

estrato
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.844648
Minimum0
Maximum6
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:42.845376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.23619
Coefficient of variation (CV)0.25516612
Kurtosis-0.7136346
Mean4.844648
Median Absolute Deviation (MAD)1
Skewness-0.66051526
Sum208378
Variance1.5281658
MonotonicityNot monotonic
2025-11-23T12:45:42.878005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
619011
44.2%
48842
20.6%
57556
 
17.6%
36085
 
14.1%
21402
 
3.3%
1105
 
0.2%
011
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
011
 
< 0.1%
1105
 
0.2%
21402
 
3.3%
36085
 
14.1%
48842
20.6%
57556
 
17.6%
619011
44.2%
ValueCountFrequency (%)
619011
44.2%
57556
 
17.6%
48842
20.6%
36085
 
14.1%
21402
 
3.3%
1105
 
0.2%
011
 
< 0.1%

antiguedad
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size2.7 MiB
MAS DE 20 ANOS
14352 
ENTRE 10 Y 20 ANOS
11536 
ENTRE 0 Y 5 ANOS
8057 
ENTRE 5 Y 10 ANOS
7021 
REMODELADO
1811 
Other values (3)
 
226

Length

Max length18
Median length17
Mean length15.766063
Min length10

Characters and Unicode

Total characters677988
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENTRE 10 Y 20 ANOS
2nd rowMAS DE 20 ANOS
3rd rowENTRE 0 Y 5 ANOS
4th rowENTRE 10 Y 20 ANOS
5th rowMAS DE 20 ANOS

Common Values

ValueCountFrequency (%)
MAS DE 20 ANOS14352
33.4%
ENTRE 10 Y 20 ANOS11536
26.8%
ENTRE 0 Y 5 ANOS8057
18.7%
ENTRE 5 Y 10 ANOS7021
16.3%
REMODELADO1811
 
4.2%
SOBRE PLANOS107
 
0.2%
EN CONSTRUCCION101
 
0.2%
PARA ESTRENAR18
 
< 0.1%
(Missing)10
 
< 0.1%

Length

2025-11-23T12:45:42.920635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:42.959627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
anos40966
21.3%
entre26614
13.8%
y26614
13.8%
2025888
13.4%
1018557
9.6%
515078
 
7.8%
mas14352
 
7.4%
de14352
 
7.4%
08057
 
4.2%
remodelado1811
 
0.9%
Other values (6)452
 
0.2%

Most occurring characters

ValueCountFrequency (%)
149738
22.1%
E71446
10.5%
N68008
10.0%
A57290
 
8.5%
S55651
 
8.2%
052502
 
7.7%
O45004
 
6.6%
R28687
 
4.2%
T26733
 
3.9%
Y26614
 
3.9%
Other values (11)96315
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)677988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
149738
22.1%
E71446
10.5%
N68008
10.0%
A57290
 
8.5%
S55651
 
8.2%
052502
 
7.7%
O45004
 
6.6%
R28687
 
4.2%
T26733
 
3.9%
Y26614
 
3.9%
Other values (11)96315
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)677988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
149738
22.1%
E71446
10.5%
N68008
10.0%
A57290
 
8.5%
S55651
 
8.2%
052502
 
7.7%
O45004
 
6.6%
R28687
 
4.2%
T26733
 
3.9%
Y26614
 
3.9%
Other values (11)96315
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)677988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
149738
22.1%
E71446
10.5%
N68008
10.0%
A57290
 
8.5%
S55651
 
8.2%
052502
 
7.7%
O45004
 
6.6%
R28687
 
4.2%
T26733
 
3.9%
Y26614
 
3.9%
Other values (11)96315
14.2%

latitud
Real number (ℝ)

High correlation 

Distinct23274
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6860989
Minimum4.4686294
Maximum4.8208222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:43.009613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.4686294
5-th percentile4.6163173
Q14.6633224
median4.688729
Q34.71198
95-th percentile4.7462208
Maximum4.8208222
Range0.3521928
Interquartile range (IQR)0.0486576

Descriptive statistics

Standard deviation0.03829695
Coefficient of variation (CV)0.0081724587
Kurtosis1.1726585
Mean4.6860989
Median Absolute Deviation (MAD)0.024754
Skewness-0.56842323
Sum201563.17
Variance0.0014666564
MonotonicityNot monotonic
2025-11-23T12:45:43.063153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.706143
 
0.3%
4.694142
 
0.3%
4.671127
 
0.3%
4.693126
 
0.3%
4.66126
 
0.3%
4.669119
 
0.3%
4.689119
 
0.3%
4.672118
 
0.3%
4.677113
 
0.3%
4.697111
 
0.3%
Other values (23264)41769
97.1%
ValueCountFrequency (%)
4.46862941
 
< 0.1%
4.4721324171
 
< 0.1%
4.4731056351
 
< 0.1%
4.4731062481
 
< 0.1%
4.47310731
 
< 0.1%
4.47315261
 
< 0.1%
4.4732679124
< 0.1%
4.4732814711
 
< 0.1%
4.4733221191
 
< 0.1%
4.47347162
< 0.1%
ValueCountFrequency (%)
4.82082221
 
< 0.1%
4.81919671
 
< 0.1%
4.81818771
 
< 0.1%
4.81753061
 
< 0.1%
4.81564241
 
< 0.1%
4.80150651
 
< 0.1%
4.8003712
< 0.1%
4.7998361
 
< 0.1%
4.799392
< 0.1%
4.7993
< 0.1%

longitud
Real number (ℝ)

High correlation 

Distinct11415
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.062808
Minimum-74.213645
Maximum-74.014
Zeros0
Zeros (%)0.0%
Negative43013
Negative (%)100.0%
Memory size336.2 KiB
2025-11-23T12:45:43.113746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.213645
5-th percentile-74.13904
Q1-74.068665
median-74.052
Q3-74.043491
95-th percentile-74.02843
Maximum-74.014
Range0.19964519
Interquartile range (IQR)0.025173788

Descriptive statistics

Standard deviation0.033544163
Coefficient of variation (CV)-0.00045291508
Kurtosis2.9429919
Mean-74.062808
Median Absolute Deviation (MAD)0.010824
Skewness-1.781807
Sum-3185663.6
Variance0.0011252109
MonotonicityNot monotonic
2025-11-23T12:45:43.163435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.049394
 
0.9%
-74.051297
 
0.7%
-74.047288
 
0.7%
-74.052285
 
0.7%
-74.048278
 
0.6%
-74.05273
 
0.6%
-74.046263
 
0.6%
-74.045261
 
0.6%
-74.043245
 
0.6%
-74.053230
 
0.5%
Other values (11405)40199
93.5%
ValueCountFrequency (%)
-74.213645191
 
< 0.1%
-74.213581
 
< 0.1%
-74.213458231
 
< 0.1%
-74.212851761
 
< 0.1%
-74.212830973
< 0.1%
-74.21264371
 
< 0.1%
-74.212641
 
< 0.1%
-74.212416572
< 0.1%
-74.212351
 
< 0.1%
-74.2121661
 
< 0.1%
ValueCountFrequency (%)
-74.0141
 
< 0.1%
-74.0161
 
< 0.1%
-74.016291
 
< 0.1%
-74.0171
 
< 0.1%
-74.0185
< 0.1%
-74.018521
 
< 0.1%
-74.01891
 
< 0.1%
-74.0193
< 0.1%
-74.019521
 
< 0.1%
-74.0196461
 
< 0.1%

direccion
Text

Missing 

Distinct703
Distinct (%)88.5%
Missing42219
Missing (%)98.2%
Memory size1.3 MiB
2025-11-23T12:45:43.269459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length32
Mean length20.905542
Min length11

Characters and Unicode

Total characters16599
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique633 ?
Unique (%)79.7%

Sample

1st rowCARRERA 55A # 163-35
2nd rowCARRERA 7F # 153 - 75
3rd rowCARRERA 69C #2 - 60
4th rowCALLE 131C # 126-95
5th rowCARRERA 5 # 187-15
ValueCountFrequency (%)
1176
27.4%
calle371
 
8.6%
carrera339
 
7.9%
sur118
 
2.7%
2028
 
0.7%
diagonal26
 
0.6%
transversal26
 
0.6%
5024
 
0.6%
7022
 
0.5%
8021
 
0.5%
Other values (718)2141
49.9%
2025-11-23T12:45:43.688910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3527
21.2%
A1455
 
8.8%
R1367
 
8.2%
1949
 
5.7%
C852
 
5.1%
L811
 
4.9%
#756
 
4.6%
-755
 
4.5%
E752
 
4.5%
5596
 
3.6%
Other values (25)4779
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)16599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3527
21.2%
A1455
 
8.8%
R1367
 
8.2%
1949
 
5.7%
C852
 
5.1%
L811
 
4.9%
#756
 
4.6%
-755
 
4.5%
E752
 
4.5%
5596
 
3.6%
Other values (25)4779
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3527
21.2%
A1455
 
8.8%
R1367
 
8.2%
1949
 
5.7%
C852
 
5.1%
L811
 
4.9%
#756
 
4.6%
-755
 
4.5%
E752
 
4.5%
5596
 
3.6%
Other values (25)4779
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3527
21.2%
A1455
 
8.8%
R1367
 
8.2%
1949
 
5.7%
C852
 
5.1%
L811
 
4.9%
#756
 
4.6%
-755
 
4.5%
E752
 
4.5%
5596
 
3.6%
Other values (25)4779
28.8%
Distinct40499
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2025-11-23T12:45:43.788483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1000
Median length721
Mean length496.95032
Min length1

Characters and Unicode

Total characters21375324
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38859 ?
Unique (%)90.3%

Sample

1st rowapartamento en venta de 76m2, con vista exterior, ubicado en un 1er piso (torre 4 apto 101), parqueadero propio cubierto (149). consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en habitaciones y zona social y ceramico en banos y cocina. el conjunto cuenta con zona social, parque infantil, zona verde, salon comunal y vigilancia privada las 24 horas. cerca a centro comercial arizona; cerca a almacenes de cadena como carulla, d1, ara y olimpica; cerca a colegio colombo brighton y el vaticano; cerca a paraderos del sitp; vias de acceso por la carrera 55a calle 163.
2nd rowapartamento en venta de 60m2, con vista interior, ubicado en un 5to piso ( apto 506), acceso por escaleras. consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y ceramico en zona social, banos y cocina. el edificio cuenta con parque infantil, zona social, salon comunal y vigilancia privada las 24 horas. cerca a centro comercial mediterraneo; cerca a almacenes de cadena exito, colsubsidio, tienda d1, tienda ara; cerca a colegio institucion educativa distrital agustin fernandez; cerca a paraderos del sitp; vias de acceso por la calle 153y carrera 7f. *valor de administracion por confirmar*
3rd rowapartamento en venta de 54m2, con vista exterior, ubicado en un 6to piso (torre 3 apto 609), acceso por escaleras y ascensor. consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con zona para ninos, zonas verdes, salon comunal y vigilancia privada las 24 horas. cerca a centros comerciales como ciudad tintal y tintal plaza; cerca a almacenes de cadena como colsubsidio, exito y d1; cerca a colegio agustiniano tagaste y psicopedagogico san sebastian; cerca a universidad publica de kennedy y universidad distrital; cerca a estacion de transmilenio biblioteca el tintal; con vias de acceso por la calle 7 y la carrera 87b.
4th rowapartamento en venta de 43 m2, con vista interior, ubicado en un 4to piso (torre 21 apto 402), acceso por escaleras. consta de 2 habitaciones, 2 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con parque infantil, salon comunal, zonas verdes y vigilancia privada las 24 horas. cerca a centro comercial plaza imperial; cerca a almacenes de cadena tienda d1, surtimax; cerca a colegio nueva colombia ied sede b; cerca a paraderos del sitp vias de acceso por la carrera 129 y calle 131c
5th rowapartamento duplex en venta de 48m2, con vista exterior, ubicado en un 2do piso (torre 10b apto 201), acceso por escaleras. consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y ceramica en zona social, banos y cocina. el conjunto cuenta con zonas verdes, zona para ninos, zonas verdes y salon comunal y vigilancia privada las 24 horas. cerca a centro comercial multiplaza 183; cerca a almacenes de cadena surtimax, d1 y ara; cerca a colegio fundacion la ensenanza; cerca a paraderos del sitp; vias de acceso por la calle 187b y cra 5.a.
ValueCountFrequency (%)
de193737
 
5.8%
con136155
 
4.1%
y135939
 
4.1%
en87461
 
2.6%
la56071
 
1.7%
el53323
 
1.6%
bano47813
 
1.4%
a47486
 
1.4%
apartamento46205
 
1.4%
zona42447
 
1.3%
Other values (37323)2473252
74.5%
2025-11-23T12:45:43.941750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3355842
15.7%
a2338068
10.9%
e1899089
 
8.9%
o1644816
 
7.7%
n1395326
 
6.5%
i1261193
 
5.9%
c1119946
 
5.2%
s1072801
 
5.0%
r1070131
 
5.0%
t889671
 
4.2%
Other values (61)5328441
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)21375324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3355842
15.7%
a2338068
10.9%
e1899089
 
8.9%
o1644816
 
7.7%
n1395326
 
6.5%
i1261193
 
5.9%
c1119946
 
5.2%
s1072801
 
5.0%
r1070131
 
5.0%
t889671
 
4.2%
Other values (61)5328441
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21375324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3355842
15.7%
a2338068
10.9%
e1899089
 
8.9%
o1644816
 
7.7%
n1395326
 
6.5%
i1261193
 
5.9%
c1119946
 
5.2%
s1072801
 
5.0%
r1070131
 
5.0%
t889671
 
4.2%
Other values (61)5328441
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21375324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3355842
15.7%
a2338068
10.9%
e1899089
 
8.9%
o1644816
 
7.7%
n1395326
 
6.5%
i1261193
 
5.9%
c1119946
 
5.2%
s1072801
 
5.0%
r1070131
 
5.0%
t889671
 
4.2%
Other values (61)5328441
24.9%

website
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.7 MiB
metrocuadrado.com
42217 
habi.co
 
794

Length

Max length17
Median length17
Mean length16.815396
Min length7

Characters and Unicode

Total characters723247
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhabi.co
2nd rowhabi.co
3rd rowhabi.co
4th rowhabi.co
5th rowhabi.co

Common Values

ValueCountFrequency (%)
metrocuadrado.com42217
98.1%
habi.co794
 
1.8%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:43.983009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:44.005128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metrocuadrado.com42217
98.2%
habi.co794
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o127445
17.6%
a85228
11.8%
c85228
11.8%
m84434
11.7%
r84434
11.7%
d84434
11.7%
.43011
 
5.9%
e42217
 
5.8%
t42217
 
5.8%
u42217
 
5.8%
Other values (3)2382
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)723247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o127445
17.6%
a85228
11.8%
c85228
11.8%
m84434
11.7%
r84434
11.7%
d84434
11.7%
.43011
 
5.9%
e42217
 
5.8%
t42217
 
5.8%
u42217
 
5.8%
Other values (3)2382
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)723247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o127445
17.6%
a85228
11.8%
c85228
11.8%
m84434
11.7%
r84434
11.7%
d84434
11.7%
.43011
 
5.9%
e42217
 
5.8%
t42217
 
5.8%
u42217
 
5.8%
Other values (3)2382
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)723247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o127445
17.6%
a85228
11.8%
c85228
11.8%
m84434
11.7%
r84434
11.7%
d84434
11.7%
.43011
 
5.9%
e42217
 
5.8%
t42217
 
5.8%
u42217
 
5.8%
Other values (3)2382
 
0.3%
Distinct43011
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Memory size336.2 KiB
Minimum2024-07-03 23:27:37.566000
Maximum2024-09-03 23:49:48.998000
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T12:45:44.044807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:44.096652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct43011
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Memory size336.2 KiB
Minimum2024-07-03 23:27:30.843000
Maximum2024-09-03 23:49:02.032000
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T12:45:44.146716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:44.198505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

url
URL

Missing 

Distinct794
Distinct (%)100.0%
Missing42219
Missing (%)98.2%
Memory size1.4 MiB
https://habi.co/page-data/venta-apartamentos/16792987321/allegro-capellania---apartamento-venta-tintal-central-fontibon/page-data.json
 
1
https://habi.co/page-data/venta-apartamentos/15771569492/gerona-del-porvenir-2---apartamento-venta-galan-kennedy/page-data.json
 
1
https://habi.co/page-data/venta-apartamentos/10952800375/reserva-fontibon-apartamento-venta-zona-franca-fontibon/page-data.json
 
1
https://habi.co/page-data/venta-apartamentos/12976302735/nova-torre-189-apartamento-venta-tibabita-usaquen/page-data.json
 
1
https://habi.co/page-data/venta-apartamentos/14313187685/laureles-apartamento-venta-cedros-usaquen/page-data.json
 
1
Other values (789)
 
789
(Missing)
42219 
ValueCountFrequency (%)
https://habi.co/page-data/venta-apartamentos/16792987321/allegro-capellania---apartamento-venta-tintal-central-fontibon/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/15771569492/gerona-del-porvenir-2---apartamento-venta-galan-kennedy/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/10952800375/reserva-fontibon-apartamento-venta-zona-franca-fontibon/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/12976302735/nova-torre-189-apartamento-venta-tibabita-usaquen/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/14313187685/laureles-apartamento-venta-cedros-usaquen/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/13359284340/reserva-v---apartamento-venta-dorado-industrial-engativa/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/11287692435/edificio-roper---apartamento-venta-canodromo-suba/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/16972797096/plaza-castilla---apartamento-venta-bosconia-kennedy/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/19293575860/parque-macarena-1---apartamento-venta-laureles-bosa/page-data.json1
 
< 0.1%
https://habi.co/page-data/venta-apartamentos/17795274862/roma-reservado-2---venta-gran-britalia-bosa/page-data.json1
 
< 0.1%
Other values (784)784
 
1.8%
(Missing)42219
98.2%
ValueCountFrequency (%)
https794
 
1.8%
(Missing)42219
98.2%
ValueCountFrequency (%)
habi.co794
 
1.8%
(Missing)42219
98.2%
ValueCountFrequency (%)
/page-data/venta-apartamentos/19249352874/sua-1---apartamento-venta-flores-suba/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/16819095321/picadilly---apartamento-venta-britalia-suba/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/14791711508/santander-apartamento-venta-barrancas-usaquen/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/19859100545/sabana-central---apartamento-venta-hipotecho-sur-kennedy/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/17650931786/condominio-1-suba---apartamento-venta-tibabuyes-suba/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/16197048374/belhorizonte---apartamento-duplex-venta-horizontes-usaquen/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/15837762206/miranda-apartamentos---apartamento-venta-ciudadela-recreo-2-bosa/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/18031830585/bellavista-imperial---apartamento-venta-tibabuyes-suba/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/17400764718/bosques-camino-verde---apartamento-venta-compartir-suba/page-data.json1
 
< 0.1%
/page-data/venta-apartamentos/7865808925/tekto-san-marcos-apartamento-venta-magdalena-teusaquillo/page-data.json1
 
< 0.1%
Other values (784)784
 
1.8%
(Missing)42219
98.2%
ValueCountFrequency (%)
794
 
1.8%
(Missing)42219
98.2%
ValueCountFrequency (%)
794
 
1.8%
(Missing)42219
98.2%

timeline
Text

Missing 

Distinct2047
Distinct (%)7.0%
Missing13586
Missing (%)31.6%
Memory size2.2 MiB
2025-11-23T12:45:44.287420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length390
Median length2
Mean length13.203894
Min length2

Characters and Unicode

Total characters388551
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2046 ?
Unique (%)7.0%

Sample

1st row[{'fecha': {'$date': '2024-07-03T23:27:30.843Z'}, 'precio_venta': 346000000}, {'fecha': {'$date': '2024-08-01T23:50:13.928Z'}, 'precio_venta': 339000000}]
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
27381
56.0%
fecha4275
 
8.8%
date4275
 
8.8%
precio_arriendo2252
 
4.6%
precio_venta2023
 
4.1%
numberlong101
 
0.2%
250000045
 
0.1%
180000043
 
0.1%
200000042
 
0.1%
120000042
 
0.1%
Other values (5641)8378
 
17.1%
2025-11-23T12:45:44.429965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
041915
 
10.8%
'34604
 
8.9%
]29427
 
7.6%
[29427
 
7.6%
:21476
 
5.5%
19430
 
5.0%
e17201
 
4.4%
215117
 
3.9%
a12825
 
3.3%
410518
 
2.7%
Other values (32)156611
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)388551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
041915
 
10.8%
'34604
 
8.9%
]29427
 
7.6%
[29427
 
7.6%
:21476
 
5.5%
19430
 
5.0%
e17201
 
4.4%
215117
 
3.9%
a12825
 
3.3%
410518
 
2.7%
Other values (32)156611
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)388551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
041915
 
10.8%
'34604
 
8.9%
]29427
 
7.6%
[29427
 
7.6%
:21476
 
5.5%
19430
 
5.0%
e17201
 
4.4%
215117
 
3.9%
a12825
 
3.3%
410518
 
2.7%
Other values (32)156611
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)388551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
041915
 
10.8%
'34604
 
8.9%
]29427
 
7.6%
[29427
 
7.6%
:21476
 
5.5%
19430
 
5.0%
e17201
 
4.4%
215117
 
3.9%
a12825
 
3.3%
410518
 
2.7%
Other values (32)156611
40.3%

estado
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing796
Missing (%)1.9%
Memory size2.2 MiB
USADO
41991 
NUEVO
 
226

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters211085
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSADO
2nd rowUSADO
3rd rowUSADO
4th rowUSADO
5th rowUSADO

Common Values

ValueCountFrequency (%)
USADO41991
97.6%
NUEVO226
 
0.5%
(Missing)796
 
1.9%

Length

2025-11-23T12:45:44.471567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:44.493797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usado41991
99.5%
nuevo226
 
0.5%

Most occurring characters

ValueCountFrequency (%)
U42217
20.0%
O42217
20.0%
A41991
19.9%
S41991
19.9%
D41991
19.9%
N226
 
0.1%
E226
 
0.1%
V226
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)211085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U42217
20.0%
O42217
20.0%
A41991
19.9%
S41991
19.9%
D41991
19.9%
N226
 
0.1%
E226
 
0.1%
V226
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)211085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U42217
20.0%
O42217
20.0%
A41991
19.9%
S41991
19.9%
D41991
19.9%
N226
 
0.1%
E226
 
0.1%
V226
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)211085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U42217
20.0%
O42217
20.0%
A41991
19.9%
S41991
19.9%
D41991
19.9%
N226
 
0.1%
E226
 
0.1%
V226
 
0.1%

compañia
Text

Missing 

Distinct880
Distinct (%)2.3%
Missing4530
Missing (%)10.5%
Memory size2.7 MiB
2025-11-23T12:45:44.567132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length62
Median length40
Mean length21.374009
Min length3

Characters and Unicode

Total characters822536
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)0.2%

Sample

1st rowINMOBILIARIOS PROFESIONALES CB
2nd rowAC ASESORES INMOBILIARIOS
3rd rowNATALIA ALEXANDRA ESCALANTE PULIDO
4th rowVIVANZA INMOBILIARIA
5th rowNATALIA ALEXANDRA ESCALANTE PULIDO
ValueCountFrequency (%)
sas8133
 
6.9%
inmobiliaria6818
 
5.8%
3446
 
2.9%
inmobiliario3035
 
2.6%
y2375
 
2.0%
s.a.s2138
 
1.8%
inmobiliarios2077
 
1.8%
inmobiliarias1885
 
1.6%
de1751
 
1.5%
engel1529
 
1.3%
Other values (1165)84542
71.8%
2025-11-23T12:45:44.705177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I99645
12.1%
A99464
12.1%
79931
9.7%
O66246
 
8.1%
S63975
 
7.8%
R57895
 
7.0%
E56793
 
6.9%
N47686
 
5.8%
L42582
 
5.2%
M29449
 
3.6%
Other values (31)178870
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)822536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I99645
12.1%
A99464
12.1%
79931
9.7%
O66246
 
8.1%
S63975
 
7.8%
R57895
 
7.0%
E56793
 
6.9%
N47686
 
5.8%
L42582
 
5.2%
M29449
 
3.6%
Other values (31)178870
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)822536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I99645
12.1%
A99464
12.1%
79931
9.7%
O66246
 
8.1%
S63975
 
7.8%
R57895
 
7.0%
E56793
 
6.9%
N47686
 
5.8%
L42582
 
5.2%
M29449
 
3.6%
Other values (31)178870
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)822536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I99645
12.1%
A99464
12.1%
79931
9.7%
O66246
 
8.1%
S63975
 
7.8%
R57895
 
7.0%
E56793
 
6.9%
N47686
 
5.8%
L42582
 
5.2%
M29449
 
3.6%
Other values (31)178870
21.7%

precio_arriendo
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1846
Distinct (%)11.7%
Missing27184
Missing (%)63.2%
Infinite0
Infinite (%)0.0%
Mean14569334
Minimum100000
Maximum1.8 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:44.755390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile1000000
Q11800000
median3500000
Q37300000
95-th percentile17890400
Maximum1.8 × 1010
Range1.79999 × 1010
Interquartile range (IQR)5500000

Descriptive statistics

Standard deviation2.8745824 × 108
Coefficient of variation (CV)19.730363
Kurtosis2588.7153
Mean14569334
Median Absolute Deviation (MAD)2091000
Skewness48.008656
Sum2.3061799 × 1011
Variance8.2632242 × 1016
MonotonicityNot monotonic
2025-11-23T12:45:44.805707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000305
 
0.7%
3500000268
 
0.6%
4500000264
 
0.6%
2000000264
 
0.6%
8000000263
 
0.6%
3000000262
 
0.6%
1100000260
 
0.6%
1000000259
 
0.6%
7000000253
 
0.6%
2500000249
 
0.6%
Other values (1836)13182
30.6%
(Missing)27184
63.2%
ValueCountFrequency (%)
1000001
 
< 0.1%
4000001
 
< 0.1%
5000002
 
< 0.1%
5200001
 
< 0.1%
5500002
 
< 0.1%
5800001
 
< 0.1%
60000012
< 0.1%
6200001
 
< 0.1%
6300002
 
< 0.1%
6400001
 
< 0.1%
ValueCountFrequency (%)
1.8 × 10101
< 0.1%
1.69 × 10101
< 0.1%
1.6 × 10101
< 0.1%
1.05 × 10101
< 0.1%
95000000001
< 0.1%
80000000001
< 0.1%
65000000002
< 0.1%
45000000002
< 0.1%
28000000001
< 0.1%
22000000001
< 0.1%

jacuzzi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
41064 
1.0
 
1947

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.041064
95.5%
1.01947
 
4.5%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:44.850747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:44.872938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.041064
95.5%
1.01947
 
4.5%

Most occurring characters

ValueCountFrequency (%)
084075
65.2%
.43011
33.3%
11947
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
084075
65.2%
.43011
33.3%
11947
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
084075
65.2%
.43011
33.3%
11947
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
084075
65.2%
.43011
33.3%
11947
 
1.5%

piso
Unsupported

Missing  Rejected  Unsupported 

Missing43013
Missing (%)100.0%
Memory size336.2 KiB

closets
Unsupported

Missing  Rejected  Unsupported 

Missing43013
Missing (%)100.0%
Memory size336.2 KiB

chimenea
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
43011 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.043011
> 99.9%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:44.901585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:44.922591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.043011
100.0%

Most occurring characters

ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

permite_mascotas
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
43011 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.043011
> 99.9%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:44.952094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:44.972636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.043011
100.0%

Most occurring characters

ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

gimnasio
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
30314 
1.0
12697 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.030314
70.5%
1.012697
29.5%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.000719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.023195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.030314
70.5%
1.012697
29.5%

Most occurring characters

ValueCountFrequency (%)
073325
56.8%
.43011
33.3%
112697
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
073325
56.8%
.43011
33.3%
112697
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
073325
56.8%
.43011
33.3%
112697
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
073325
56.8%
.43011
33.3%
112697
 
9.8%

ascensor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
1.0
27016 
0.0
15995 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.027016
62.8%
0.015995
37.2%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.053039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.076255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.027016
62.8%
0.015995
37.2%

Most occurring characters

ValueCountFrequency (%)
059006
45.7%
.43011
33.3%
127016
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
059006
45.7%
.43011
33.3%
127016
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
059006
45.7%
.43011
33.3%
127016
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
059006
45.7%
.43011
33.3%
127016
20.9%

conjunto_cerrado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
26216 
1.0
16795 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.026216
60.9%
1.016795
39.0%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.106339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.130367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.026216
61.0%
1.016795
39.0%

Most occurring characters

ValueCountFrequency (%)
069227
53.7%
.43011
33.3%
116795
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
069227
53.7%
.43011
33.3%
116795
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
069227
53.7%
.43011
33.3%
116795
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
069227
53.7%
.43011
33.3%
116795
 
13.0%

piscina
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
39122 
1.0
 
3889

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039122
91.0%
1.03889
 
9.0%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.162774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.187021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.039122
91.0%
1.03889
 
9.0%

Most occurring characters

ValueCountFrequency (%)
082133
63.7%
.43011
33.3%
13889
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
082133
63.7%
.43011
33.3%
13889
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
082133
63.7%
.43011
33.3%
13889
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
082133
63.7%
.43011
33.3%
13889
 
3.0%

salon_comunal
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
43011 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.043011
> 99.9%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.216789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.238007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.043011
100.0%

Most occurring characters

ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
086022
66.7%
.43011
33.3%

terraza
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
0.0
42967 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.042967
99.9%
1.044
 
0.1%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.265880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.288582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.042967
99.9%
1.044
 
0.1%

Most occurring characters

ValueCountFrequency (%)
085978
66.6%
.43011
33.3%
144
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
085978
66.6%
.43011
33.3%
144
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
085978
66.6%
.43011
33.3%
144
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
085978
66.6%
.43011
33.3%
144
 
< 0.1%

vigilancia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.1 MiB
1.0
25264 
0.0
17747 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.025264
58.7%
0.017747
41.3%
(Missing)2
 
< 0.1%

Length

2025-11-23T12:45:45.317178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.339887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.025264
58.7%
0.017747
41.3%

Most occurring characters

ValueCountFrequency (%)
060758
47.1%
.43011
33.3%
125264
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
060758
47.1%
.43011
33.3%
125264
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
060758
47.1%
.43011
33.3%
125264
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)129033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
060758
47.1%
.43011
33.3%
125264
19.6%

coords_modified
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.1 KiB
False
42159 
True
 
854
ValueCountFrequency (%)
False42159
98.0%
True854
 
2.0%
2025-11-23T12:45:45.357712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

localidad
Categorical

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
USAQUEN
14831 
CHAPINERO
13032 
SUBA
6828 
FONTIBON
1645 
KENNEDY
 
1320
Other values (14)
5357 

Length

Max length18
Median length14
Mean length7.5529491
Min length4

Characters and Unicode

Total characters324875
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUBA
2nd rowUSAQUEN
3rd rowLOS MARTIRES
4th rowSUBA
5th rowUSAQUEN

Common Values

ValueCountFrequency (%)
USAQUEN14831
34.5%
CHAPINERO13032
30.3%
SUBA6828
15.9%
FONTIBON1645
 
3.8%
KENNEDY1320
 
3.1%
ENGATIVA1242
 
2.9%
TEUSAQUILLO1144
 
2.7%
SANTA FE671
 
1.6%
BOSA421
 
1.0%
PUENTE ARANDA325
 
0.8%
Other values (9)1554
 
3.6%

Length

2025-11-23T12:45:45.393961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usaquen14831
32.6%
chapinero13032
28.6%
suba6828
15.0%
fontibon1645
 
3.6%
kennedy1320
 
2.9%
engativa1242
 
2.7%
teusaquillo1144
 
2.5%
santa671
 
1.5%
fe671
 
1.5%
bosa421
 
0.9%
Other values (17)3694
 
8.1%

Most occurring characters

ValueCountFrequency (%)
A43354
13.3%
U40297
12.4%
N37357
11.5%
E35306
10.9%
S25413
7.8%
O19600
 
6.0%
I19440
 
6.0%
Q15975
 
4.9%
R15750
 
4.8%
C13530
 
4.2%
Other values (14)58853
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)324875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A43354
13.3%
U40297
12.4%
N37357
11.5%
E35306
10.9%
S25413
7.8%
O19600
 
6.0%
I19440
 
6.0%
Q15975
 
4.9%
R15750
 
4.8%
C13530
 
4.2%
Other values (14)58853
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)324875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A43354
13.3%
U40297
12.4%
N37357
11.5%
E35306
10.9%
S25413
7.8%
O19600
 
6.0%
I19440
 
6.0%
Q15975
 
4.9%
R15750
 
4.8%
C13530
 
4.2%
Other values (14)58853
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)324875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A43354
13.3%
U40297
12.4%
N37357
11.5%
E35306
10.9%
S25413
7.8%
O19600
 
6.0%
I19440
 
6.0%
Q15975
 
4.9%
R15750
 
4.8%
C13530
 
4.2%
Other values (14)58853
18.1%

barrio
Text

Distinct1666
Distinct (%)3.9%
Missing193
Missing (%)0.4%
Memory size2.7 MiB
2025-11-23T12:45:45.481004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length43
Mean length16.504951
Min length3

Characters and Unicode

Total characters706742
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique432 ?
Unique (%)1.0%

Sample

1st rowURB. PICADELLY
2nd rowBARRANCAS PERLAS DEL NORTE
3rd rowSAN VICTORINO
4th rowLA ESTRELLA II
5th rowHORIZONTES USAQUEN
ValueCountFrequency (%)
s.c11006
 
9.1%
chico6226
 
5.2%
santa6124
 
5.1%
el5979
 
5.0%
norte4659
 
3.9%
barbara4059
 
3.4%
sector3862
 
3.2%
la3542
 
2.9%
de2483
 
2.1%
los2280
 
1.9%
Other values (1168)70253
58.3%
2025-11-23T12:45:45.617812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A93737
13.3%
77666
11.0%
R51688
 
7.3%
C51344
 
7.3%
E50787
 
7.2%
I49078
 
6.9%
S47060
 
6.7%
O46639
 
6.6%
L40487
 
5.7%
N40414
 
5.7%
Other values (30)157842
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)706742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A93737
13.3%
77666
11.0%
R51688
 
7.3%
C51344
 
7.3%
E50787
 
7.2%
I49078
 
6.9%
S47060
 
6.7%
O46639
 
6.6%
L40487
 
5.7%
N40414
 
5.7%
Other values (30)157842
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)706742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A93737
13.3%
77666
11.0%
R51688
 
7.3%
C51344
 
7.3%
E50787
 
7.2%
I49078
 
6.9%
S47060
 
6.7%
O46639
 
6.6%
L40487
 
5.7%
N40414
 
5.7%
Other values (30)157842
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)706742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A93737
13.3%
77666
11.0%
R51688
 
7.3%
C51344
 
7.3%
E50787
 
7.2%
I49078
 
6.9%
S47060
 
6.7%
O46639
 
6.6%
L40487
 
5.7%
N40414
 
5.7%
Other values (30)157842
22.3%
Distinct146
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2025-11-23T12:45:45.697677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length33
Mean length12.545928
Min length3

Characters and Unicode

Total characters539638
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowToberin - Foundever
2nd rowMazurén
3rd rowDe La Sabana
4th rowPortal Suba
5th rowTerminal
ValueCountFrequency (%)
calle18248
 
17.8%
7024
 
6.9%
1273767
 
3.7%
3152
 
3.1%
portal3081
 
3.0%
virrey2753
 
2.7%
1062661
 
2.6%
sierra2636
 
2.6%
pepe2636
 
2.6%
1002610
 
2.5%
Other values (208)53950
52.6%
2025-11-23T12:45:45.829250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59505
 
11.0%
l53693
 
9.9%
e52522
 
9.7%
a51725
 
9.6%
r32505
 
6.0%
o28859
 
5.3%
C22754
 
4.2%
i22705
 
4.2%
113449
 
2.5%
s12179
 
2.3%
Other values (60)189742
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)539638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
59505
 
11.0%
l53693
 
9.9%
e52522
 
9.7%
a51725
 
9.6%
r32505
 
6.0%
o28859
 
5.3%
C22754
 
4.2%
i22705
 
4.2%
113449
 
2.5%
s12179
 
2.3%
Other values (60)189742
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)539638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
59505
 
11.0%
l53693
 
9.9%
e52522
 
9.7%
a51725
 
9.6%
r32505
 
6.0%
o28859
 
5.3%
C22754
 
4.2%
i22705
 
4.2%
113449
 
2.5%
s12179
 
2.3%
Other values (60)189742
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)539638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
59505
 
11.0%
l53693
 
9.9%
e52522
 
9.7%
a51725
 
9.6%
r32505
 
6.0%
o28859
 
5.3%
C22754
 
4.2%
i22705
 
4.2%
113449
 
2.5%
s12179
 
2.3%
Other values (60)189742
35.2%

distancia_estacion_tm_m
Real number (ℝ)

High correlation 

Distinct25713
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1270.5159
Minimum4.94
Maximum7095.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:45.874914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.94
5-th percentile234.29
Q1578.38
median1175.52
Q31707.92
95-th percentile2864.532
Maximum7095.66
Range7090.72
Interquartile range (IQR)1129.54

Descriptive statistics

Standard deviation841.81401
Coefficient of variation (CV)0.6625765
Kurtosis2.3527397
Mean1270.5159
Median Absolute Deviation (MAD)568.27
Skewness1.1287687
Sum54648702
Variance708650.83
MonotonicityNot monotonic
2025-11-23T12:45:45.924381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1399.9272
 
0.2%
749.4939
 
0.1%
2370.3738
 
0.1%
435.931
 
0.1%
1222.2430
 
0.1%
3027.7529
 
0.1%
848.8729
 
0.1%
1518.7228
 
0.1%
3307.0827
 
0.1%
2883.8426
 
0.1%
Other values (25703)42664
99.2%
ValueCountFrequency (%)
4.941
 
< 0.1%
5.661
 
< 0.1%
11.831
 
< 0.1%
16.271
 
< 0.1%
17.21
 
< 0.1%
18.931
 
< 0.1%
19.244
 
< 0.1%
21.4524
0.1%
21.751
 
< 0.1%
22.771
 
< 0.1%
ValueCountFrequency (%)
7095.661
 
< 0.1%
6727.131
 
< 0.1%
6632.151
 
< 0.1%
6595.421
 
< 0.1%
6590.641
 
< 0.1%
6583.632
< 0.1%
6582.464
< 0.1%
6580.791
 
< 0.1%
6575.691
 
< 0.1%
6567.341
 
< 0.1%

is_cerca_estacion_tm
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
34002 
1
9011 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43013
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034002
79.1%
19011
 
20.9%

Length

2025-11-23T12:45:45.968000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:45.990345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034002
79.1%
19011
 
20.9%

Most occurring characters

ValueCountFrequency (%)
034002
79.1%
19011
 
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)43013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034002
79.1%
19011
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034002
79.1%
19011
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034002
79.1%
19011
 
20.9%
Distinct176
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
2025-11-23T12:45:46.069838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length84
Median length63
Mean length36.007207
Min length19

Characters and Unicode

Total characters1548778
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowPARQUE VECINAL DESARROLLO CANTALEJO URBANIZACIÓN CANTALEJO MANZANA B
2nd rowPARQUE ZONAL ALTA BLANCA
3rd rowPARQUE METROPOLITANO TERCER MILENIO
4th rowPARQUE ZONAL LA GAITANA
5th rowPARQUE VECINAL DESARROLLO VERBENAL I
ValueCountFrequency (%)
parque44093
19.7%
vecinal16608
 
7.4%
zonal16091
 
7.2%
el15578
 
7.0%
urbanización12503
 
5.6%
metropolitano9817
 
4.4%
country6818
 
3.0%
la6007
 
2.7%
ii5203
 
2.3%
cabrera3972
 
1.8%
Other values (280)87044
38.9%
2025-11-23T12:45:46.217469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A199300
12.9%
180721
11.7%
E134299
 
8.7%
R123623
 
8.0%
N114916
 
7.4%
I101518
 
6.6%
O95672
 
6.2%
L90921
 
5.9%
U76084
 
4.9%
C68544
 
4.4%
Other values (30)363180
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1548778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A199300
12.9%
180721
11.7%
E134299
 
8.7%
R123623
 
8.0%
N114916
 
7.4%
I101518
 
6.6%
O95672
 
6.2%
L90921
 
5.9%
U76084
 
4.9%
C68544
 
4.4%
Other values (30)363180
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1548778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A199300
12.9%
180721
11.7%
E134299
 
8.7%
R123623
 
8.0%
N114916
 
7.4%
I101518
 
6.6%
O95672
 
6.2%
L90921
 
5.9%
U76084
 
4.9%
C68544
 
4.4%
Other values (30)363180
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1548778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A199300
12.9%
180721
11.7%
E134299
 
8.7%
R123623
 
8.0%
N114916
 
7.4%
I101518
 
6.6%
O95672
 
6.2%
L90921
 
5.9%
U76084
 
4.9%
C68544
 
4.4%
Other values (30)363180
23.4%

distancia_parque_m
Real number (ℝ)

High correlation 

Distinct24931
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.88477
Minimum0.22
Maximum6276.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.2 KiB
2025-11-23T12:45:46.262503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile193.73
Q1470.14
median750.14
Q31087.73
95-th percentile1622.3
Maximum6276.51
Range6276.29
Interquartile range (IQR)617.59

Descriptive statistics

Standard deviation447.79621
Coefficient of variation (CV)0.55359704
Kurtosis3.8796945
Mean808.88477
Median Absolute Deviation (MAD)304.92
Skewness0.99911752
Sum34792560
Variance200521.45
MonotonicityNot monotonic
2025-11-23T12:45:46.310243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
659.4775
 
0.2%
633.6538
 
0.1%
1242.5734
 
0.1%
491.4733
 
0.1%
37.9431
 
0.1%
1423.3430
 
0.1%
689.8330
 
0.1%
920.3929
 
0.1%
1830.5827
 
0.1%
674.8427
 
0.1%
Other values (24921)42659
99.2%
ValueCountFrequency (%)
0.224
< 0.1%
0.272
 
< 0.1%
4.661
 
< 0.1%
10.151
 
< 0.1%
10.271
 
< 0.1%
12.511
 
< 0.1%
12.851
 
< 0.1%
13.412
 
< 0.1%
13.766
< 0.1%
13.821
 
< 0.1%
ValueCountFrequency (%)
6276.511
 
< 0.1%
6168.021
 
< 0.1%
5996.91
 
< 0.1%
5865.31
 
< 0.1%
5647.131
 
< 0.1%
4876.812
< 0.1%
4844.481
 
< 0.1%
4793.972
< 0.1%
4737.573
< 0.1%
4719.711
 
< 0.1%

is_cerca_parque
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
31183 
1
11830 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43013
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
031183
72.5%
111830
 
27.5%

Length

2025-11-23T12:45:46.354195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T12:45:46.378237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
031183
72.5%
111830
 
27.5%

Most occurring characters

ValueCountFrequency (%)
031183
72.5%
111830
 
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)43013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
031183
72.5%
111830
 
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
031183
72.5%
111830
 
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
031183
72.5%
111830
 
27.5%

Interactions

2025-11-23T12:45:40.034363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.193168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.833743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.391711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.927167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.442652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.003652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.849791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.392244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.099211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.824574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.438762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.284781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.236773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.874793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.435571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.970216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.488513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.042718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.890365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.441448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.175402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.886217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.506049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.331510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.276706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.921030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.477999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.016267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.535479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.089801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.933752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.492829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.228458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.942612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.557989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.373996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.318367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.961920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.524668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.057470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.580192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.135138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.977096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.558321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.283847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.990974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.607344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.418994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.358473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.007594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.570996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.098334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.623934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.174013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.024710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.615232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.335705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.036205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.657511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.466244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.403561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.055776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.614715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.141625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.670082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.222368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.078686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.660521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.401419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.088849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.706010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.511930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.442900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.100158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.657334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.185955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.713182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.273186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.127272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.706444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.458245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.155058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.754472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.555716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.483089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.141680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.699827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.226629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.760263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.354193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.174702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.758937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.521090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.203960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.802033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.605661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.523309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.187628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.744529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.271433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.807755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.417385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.212489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.811812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.578555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.249386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.849258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.652865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.570500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.236499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.788982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.315563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.854748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.491801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.256101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.865648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.639905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.298377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.893940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.700766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.611241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.301746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.838530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.357816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.902607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.567956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.304110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.939775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.701444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.341666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.939388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:40.752934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:33.791958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.349116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:34.887360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.403697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:35.953913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:36.619828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:37.349267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.014191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:38.764901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.388070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T12:45:39.986565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-23T12:45:46.420069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
administracionantiguedadareaascensorbanosconjunto_cerradocoords_modifieddistancia_estacion_tm_mdistancia_parque_mestadoestratogimnasiohabitacionesis_cerca_estacion_tmis_cerca_parquejacuzzilatitudlocalidadlongitudparqueaderospiscinaprecio_arriendoprecio_ventaterrazatipo_operaciontipo_propiedadvigilanciawebsite
administracion1.0000.0000.8460.0110.7580.0000.0000.1130.0801.0000.7720.0140.3850.0000.0130.026-0.1200.0000.4400.7980.0260.8380.8880.0000.0170.0000.0080.000
antiguedad0.0001.0000.0000.1200.1030.1260.0300.0610.0351.0000.0880.2700.1340.0750.0360.0260.0790.1020.0790.0880.2120.0010.0000.0110.0800.0230.0500.052
area0.8460.0001.0000.0000.8680.0000.0000.1400.0470.0000.6780.0000.6350.0000.0000.000-0.0040.0000.3970.8210.0000.8650.9130.0000.0000.0000.0000.000
ascensor0.0110.1200.0001.0000.2040.0370.0000.0560.0700.0410.2950.2600.0500.0070.0480.0980.1580.2470.2310.2060.1510.0060.0070.0410.0630.0520.1930.178
banos0.7580.1030.8680.2041.0000.0780.0000.1270.0710.0670.6230.1980.6070.1270.0340.2430.0480.2130.3750.7780.1440.8110.8210.0190.1430.0230.1730.164
conjunto_cerrado0.0000.1260.0000.0370.0781.0000.0220.1090.0210.0130.0940.2430.1090.0590.0000.0900.1750.1680.1290.0170.1590.0000.0120.0310.1180.0370.1620.137
coords_modified0.0000.0300.0000.0000.0000.0221.0000.0820.0660.0080.0500.0000.0240.0430.0410.0000.0630.0940.1070.0000.0060.0000.0000.0000.0050.0000.0000.018
distancia_estacion_tm_m0.1130.0610.1400.0560.1270.1090.0821.0000.1580.0730.1160.0890.1450.7800.1890.0800.1700.3170.4620.1130.0910.1380.0890.0170.0380.0120.0610.175
distancia_parque_m0.0800.0350.0470.0700.0710.0210.0660.1581.0000.0400.1330.0280.0040.0590.7780.0340.1250.1320.2230.0900.0610.0020.0750.0000.0360.0090.0290.046
estado1.0001.0000.0000.0410.0670.0130.0080.0730.0401.0000.0610.0610.0590.0070.0120.0150.0530.0720.0640.0730.0451.0000.0001.0000.0561.0000.0151.000
estrato0.7720.0880.6780.2950.6230.0940.0500.1160.1330.0611.0000.1700.1750.1980.0930.115-0.0150.4150.5310.7040.0820.8060.7730.0360.0770.0880.2020.294
gimnasio0.0140.2700.0000.2600.1980.2430.0000.0890.0280.0610.1701.0000.0730.0320.0280.1570.1230.1380.1320.1880.3620.0100.0110.0050.0790.0250.1920.065
habitaciones0.3850.1340.6350.0500.6070.1090.0240.1450.0040.0590.1750.0731.0000.1490.0500.1660.1100.1180.0870.4450.0880.4410.4110.1500.1640.2240.0710.072
is_cerca_estacion_tm0.0000.0750.0000.0070.1270.0590.0430.7800.0590.0070.1980.0320.1491.0000.0180.0170.2530.3580.4710.1020.0280.0000.0000.0000.0240.0180.0330.024
is_cerca_parque0.0130.0360.0000.0480.0340.0000.0410.1890.7780.0120.0930.0280.0500.0181.0000.0150.2830.2510.1570.0380.0150.0080.0000.0000.0440.0160.0080.026
jacuzzi0.0260.0260.0000.0980.2430.0900.0000.0800.0340.0150.1150.1570.1660.0170.0151.0000.0670.0740.0870.1450.1820.0450.0290.0020.0490.0060.0790.029
latitud-0.1200.079-0.0040.1580.0480.1750.0630.1700.1250.053-0.0150.1230.1100.2530.2830.0671.0000.6100.3680.0480.090-0.055-0.1160.0280.0920.0340.1220.214
localidad0.0000.1020.0000.2470.2130.1680.0940.3170.1320.0720.4150.1380.1180.3580.2510.0740.6101.0000.5820.2580.1330.0000.0000.0380.1190.0700.1800.322
longitud0.4400.0790.3970.2310.3750.1290.1070.4620.2230.0640.5310.1320.0870.4710.1570.0870.3680.5821.0000.4340.1170.4650.4230.0390.0490.0730.1710.313
parqueaderos0.7980.0880.8210.2060.7780.0170.0000.1130.0900.0730.7040.1880.4450.1020.0380.1450.0480.2580.4341.0000.0980.8460.8380.0310.0810.0290.1650.143
piscina0.0260.2120.0000.1510.1440.1590.0060.0910.0610.0450.0820.3620.0880.0280.0150.1820.0900.1330.1170.0981.0000.0350.0000.0070.0650.0110.0610.043
precio_arriendo0.8380.0010.8650.0060.8110.0000.0000.1380.0021.0000.8060.0100.4410.0000.0080.045-0.0550.0000.4650.8460.0351.0000.8531.0000.0911.0000.0161.000
precio_venta0.8880.0000.9130.0070.8210.0120.0000.0890.0750.0000.7730.0110.4110.0000.0000.029-0.1160.0000.4230.8380.0000.8531.0000.0000.0000.0000.0120.000
terraza0.0000.0110.0000.0410.0190.0310.0000.0170.0001.0000.0360.0050.1500.0000.0000.0020.0280.0380.0390.0310.0071.0000.0001.0000.0230.1800.0370.231
tipo_operacion0.0170.0800.0000.0630.1430.1180.0050.0380.0360.0560.0770.0790.1640.0240.0440.0490.0920.1190.0490.0810.0650.0910.0000.0231.0000.0210.0290.104
tipo_propiedad0.0000.0230.0000.0520.0230.0370.0000.0120.0091.0000.0880.0250.2240.0180.0160.0060.0340.0700.0730.0290.0111.0000.0000.1800.0211.0000.0480.294
vigilancia0.0080.0500.0000.1930.1730.1620.0000.0610.0290.0150.2020.1920.0710.0330.0080.0790.1220.1800.1710.1650.0610.0160.0120.0370.0290.0481.0000.163
website0.0000.0520.0000.1780.1640.1370.0180.1750.0461.0000.2940.0650.0720.0240.0260.0290.2140.3220.3130.1430.0431.0000.0000.2310.1040.2940.1631.000

Missing values

2025-11-23T12:45:40.870207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-23T12:45:41.075058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-23T12:45:41.405003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_idcodigotipo_propiedadtipo_operacionprecio_ventaareahabitacionesbanosadministracionparqueaderossectorestratoantiguedadlatitudlongituddirecciondescripcionwebsitelast_viewdatetimeurltimelineestadocompañiaprecio_arriendojacuzzipisoclosetschimeneapermite_mascotasgimnasioascensorconjunto_cerradopiscinasalon_comunalterrazavigilanciacoords_modifiedlocalidadbarrioestacion_tm_cercanadistancia_estacion_tm_mis_cerca_estacion_tmparque_cercanodistancia_parque_mis_cerca_parque
066d86c7eceda690e855087604133491APARTAMENTOVENTA339000000.076.03.02.0300000.01.0BRITALIA3.0ENTRE 10 Y 20 ANOS4.746592-74.057571CARRERA 55A # 163-35apartamento en venta de 76m2, con vista exterior, ubicado en un 1er piso (torre 4 apto 101), parqueadero propio cubierto (149). consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en habitaciones y zona social y ceramico en banos y cocina. el conjunto cuenta con zona social, parque infantil, zona verde, salon comunal y vigilancia privada las 24 horas. cerca a centro comercial arizona; cerca a almacenes de cadena como carulla, d1, ara y olimpica; cerca a colegio colombo brighton y el vaticano; cerca a paraderos del sitp; vias de acceso por la carrera 55a calle 163.habi.co2024-08-13 10:34:47.3152024-07-03 23:27:30.843https://habi.co/page-data/venta-apartamentos/16819095321/picadilly---apartamento-venta-britalia-suba/page-data.json[{'fecha': {'$date': '2024-07-03T23:27:30.843Z'}, 'precio_venta': 346000000}, {'fecha': {'$date': '2024-08-01T23:50:13.928Z'}, 'precio_venta': 339000000}]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseSUBAURB. PICADELLYToberin - Foundever1142.450PARQUE VECINAL DESARROLLO CANTALEJO URBANIZACIÓN CANTALEJO MANZANA B426.091
166d86c7eceda690e855087613889852APARTAMENTOVENTA223000000.063.03.02.0NaN0.0SAN CRISTOBAL NORTE3.0MAS DE 20 ANOS4.730111-74.028170CARRERA 7F # 153 - 75apartamento en venta de 60m2, con vista interior, ubicado en un 5to piso ( apto 506), acceso por escaleras. consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y ceramico en zona social, banos y cocina. el edificio cuenta con parque infantil, zona social, salon comunal y vigilancia privada las 24 horas. cerca a centro comercial mediterraneo; cerca a almacenes de cadena exito, colsubsidio, tienda d1, tienda ara; cerca a colegio institucion educativa distrital agustin fernandez; cerca a paraderos del sitp; vias de acceso por la calle 153y carrera 7f. *valor de administracion por confirmar*habi.co2024-09-03 23:46:46.5472024-07-03 23:27:31.667https://habi.co/page-data/venta-apartamentos/14791711508/santander-apartamento-venta-barrancas-usaquen/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.00.00.00.0FalseUSAQUENBARRANCAS PERLAS DEL NORTEMazurén2384.890PARQUE ZONAL ALTA BLANCA472.471
266d86c7eceda690e855087624386770APARTAMENTOVENTA440898168.054.03.02.0305000.00.0LA SABANA3.0ENTRE 0 Y 5 ANOS4.607378-74.082648CARRERA 69C #2 - 60apartamento en venta de 54m2, con vista exterior, ubicado en un 6to piso (torre 3 apto 609), acceso por escaleras y ascensor. consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con zona para ninos, zonas verdes, salon comunal y vigilancia privada las 24 horas. cerca a centros comerciales como ciudad tintal y tintal plaza; cerca a almacenes de cadena como colsubsidio, exito y d1; cerca a colegio agustiniano tagaste y psicopedagogico san sebastian; cerca a universidad publica de kennedy y universidad distrital; cerca a estacion de transmilenio biblioteca el tintal; con vias de acceso por la calle 7 y la carrera 87b.habi.co2024-07-10 18:19:39.7492024-07-03 23:27:31.886https://habi.co/page-data/venta-apartamentos/19859100545/sabana-central---apartamento-venta-hipotecho-sur-kennedy/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseLOS MARTIRESSAN VICTORINODe La Sabana232.221PARQUE METROPOLITANO TERCER MILENIO961.290
366d86c7eceda690e855087634210060APARTAMENTOVENTA158000000.043.02.02.0106600.00.0TIBABUYES2.0ENTRE 10 Y 20 ANOS4.740109-74.113675CALLE 131C # 126-95apartamento en venta de 43 m2, con vista interior, ubicado en un 4to piso (torre 21 apto 402), acceso por escaleras. consta de 2 habitaciones, 2 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con parque infantil, salon comunal, zonas verdes y vigilancia privada las 24 horas. cerca a centro comercial plaza imperial; cerca a almacenes de cadena tienda d1, surtimax; cerca a colegio nueva colombia ied sede b; cerca a paraderos del sitp vias de acceso por la carrera 129 y calle 131chabi.co2024-07-10 18:19:03.0612024-07-03 23:27:32.141https://habi.co/page-data/venta-apartamentos/17650931786/condominio-1-suba---apartamento-venta-tibabuyes-suba/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseSUBALA ESTRELLA IIPortal Suba2275.080PARQUE ZONAL LA GAITANA539.980
466d86c7eceda690e855087644063762APARTAMENTOVENTA222800000.048.03.02.0151000.00.0VERBENAL3.0MAS DE 20 ANOS4.763900-74.025280CARRERA 5 # 187-15apartamento duplex en venta de 48m2, con vista exterior, ubicado en un 2do piso (torre 10b apto 201), acceso por escaleras. consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y ceramica en zona social, banos y cocina. el conjunto cuenta con zonas verdes, zona para ninos, zonas verdes y salon comunal y vigilancia privada las 24 horas. cerca a centro comercial multiplaza 183; cerca a almacenes de cadena surtimax, d1 y ara; cerca a colegio fundacion la ensenanza; cerca a paraderos del sitp; vias de acceso por la calle 187b y cra 5.a.habi.co2024-09-03 23:46:46.5922024-07-03 23:27:32.361https://habi.co/page-data/venta-apartamentos/16197048374/belhorizonte---apartamento-duplex-venta-horizontes-usaquen/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseUSAQUENHORIZONTES USAQUENTerminal2099.160PARQUE VECINAL DESARROLLO VERBENAL I1661.140
566d86c7eceda690e855087654006619APARTAMENTOVENTA128900000.047.02.01.086500.00.0TINTAL SUR2.0ENTRE 10 Y 20 ANOS4.632698-74.198111CARRERA 95 # 65-49SURapartamento en venta de 47m2, con vista interior, ubicado en un 6to piso (apto 628), acceso por escaleras, parqueadero comunal. consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y zona social y ceramico en banos y cocina. el conjunto cuenta con zonas verdes, zona infantil, y vigilancia privada las 24 horas. cerca a centro comercial metro recreo, metro bosa; cerca a almacenes de cadena como d1 y ara; cerca a colegio gimnasio real americano, colegio plaza logistica; cerca a universidad distrital francisco jose de caldas sede bosa; cerca a estacion de transmilenio portal americas; vias de acceso por la carrera 95a y la calle 63 sur.habi.co2024-08-13 10:34:39.6842024-07-03 23:27:32.582https://habi.co/page-data/venta-apartamentos/15837762206/miranda-apartamentos---apartamento-venta-ciudadela-recreo-2-bosa/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseBOSAS.C. SAN BERNARDINO XVIPortal Américas2800.970PARQUE VECINAL URBANIZACIÓN SANTIAGO DE ATALAYAS404.781
666d86c7eceda690e855087664245218APARTAMENTOVENTA190000000.038.02.01.0178600.00.0SUBA3.0ENTRE 5 Y 10 ANOS4.753458-74.093288CALLE 152B # 104 - 50apartamento en venta de 38m2, con vista interior, ubicado en un 1mer piso (torre 6 apto 103), acceso por ascensor, parqueadero comunal. consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con salon social, terraza bbq, gimnasio, parque infantil y vigilancia privada las 24 horas. cerca a centro comercial plaza imperial; cerca a almacenes de cadena como zapatoca, d1, exito; cerca a colegio ciedi; cerca a universidad uniagustiniana, corpas; cerca a estacion de transmilenio portal de suba y paraderos del sitp; vias de acceso por avenida ciudad de cali y calle 152b.habi.co2024-08-18 14:49:40.4722024-07-03 23:27:32.806https://habi.co/page-data/venta-apartamentos/18031830585/bellavista-imperial---apartamento-venta-tibabuyes-suba/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.01.00.0FalseSUBAPINOS DE LOMBARDIAPortal Suba746.740PARQUE VECINAL URBANIZACIÓN VALLE DE REFOUS664.930
766d86c7eceda690e855087674183584APARTAMENTOVENTA149000000.037.02.01.0160000.00.0SUBA3.0ENTRE 10 Y 20 ANOS4.759587-74.100971AVENIDA CALLE 153 # 115-80apartamento en venta de 37m2, con vista exterior, ubicado en un 4to piso (torre 10 apto 402), acceso por escaleras. consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con zonas verdes, parque infantil, zona social y vigilancia privada las 24 horas. cerca a centros comerciales como plaza imperial; cerca a almacenes de cadena como surtimax, tienda d1, tienda ara; cerca a colegio el salitre suba - sede c; cerca a paraderos del sitp; vias de acceso por la carrera 111a y avenida calle 153.habi.co2024-07-07 11:10:46.7932024-07-03 23:27:33.025https://habi.co/page-data/venta-apartamentos/17400764718/bosques-camino-verde---apartamento-venta-compartir-suba/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseSUBAURB. CAMINO VERDEPortal Suba1602.070PARQUE VECINAL CIUDADELA CAFAM ANTES URBANIZACIÓN PUERTO SOL1309.040
866d86c7eceda690e855087682140282APARTAMENTOVENTA350000000.051.01.02.0NaN1.0CHAPINERO4.0ENTRE 5 Y 10 ANOS4.603247-74.118735CARRERA 13 #40B - 74apartamento en venta de 51m2, con vista interior, ubicado en un 2do piso ( apto 219), acceso por ascensor, parqueadero propio cubierto (21). consta de 1 habitacion, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones, zona social y ceramico en banos y cocina. el edificio cuenta con salon comunal, zonas verdes, zona bbq, terraza, gimnasio y vigilancia privada las 24 horas. cerca a centro comercial san martin; cerca a almacenes de cadena como exito, d1 y oxxo; cerca a colegio champagnat; cerca a universidad javeriana; cerca a estacion de transmilenio avenida 39 y paraderos del sitp; vias de acceso por avenida carrera 7 y avenida caracas. valor de administracion por confirmarhabi.co2024-09-03 23:46:38.7792024-07-03 23:27:33.270https://habi.co/page-data/venta-apartamentos/7865808925/tekto-san-marcos-apartamento-venta-magdalena-teusaquillo/page-data.json[{'fecha': {'$date': '2024-07-03T23:27:33.270Z'}, 'precio_venta': 366000000}, {'fecha': {'$date': '2024-08-18T14:48:41.970Z'}, 'precio_venta': 350000000}]NaNNaNNaN0.0NaNNaN0.00.01.00.01.00.00.01.00.0FalsePUENTE ARANDACIUDAD MONTES III SECTORNQS - Calle 30 S944.980PARQUE ZONAL CIUDAD MONTES396.591
966d86c7eceda690e855087692152996CASA CON CONJUNTO CERRADOVENTA190000000.057.02.01.0155000.00.0GARCES NAVAS2.0ENTRE 10 Y 20 ANOS4.721302-74.134498DIAGONAL 77 # 123 A - 88casa en venta de 57m2, con vista exterior, (casa13), acceso directo desde el exterior. casa en obra gris consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. el conjunto cuenta con salon social, terraza bbq, parque infantil y vigilancia privada las 24 horas. cerca a centro comercial unicentro de occidente cerca a almacenes de cadena como carulla y exito; cerca a colegio catolico de la sabana; cerca a paraderos del sitp; vias de acceso por la calle 80 y diagonal 77.habi.co2024-09-03 23:46:32.3682024-07-03 23:27:33.493https://habi.co/page-data/venta-apartamentos/7900245689/quintas-sabana-2-casa-venta-dorado-engativa/page-data.json[]NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.00.01.00.0FalseENGATIVAEL GACO - EL PORVENIR (PREDIO)Portal 802948.930PARQUE METROPOLITANO PLANTA DE TRATAMIENTO SALITRE1227.520
_idcodigotipo_propiedadtipo_operacionprecio_ventaareahabitacionesbanosadministracionparqueaderossectorestratoantiguedadlatitudlongituddirecciondescripcionwebsitelast_viewdatetimeurltimelineestadocompañiaprecio_arriendojacuzzipisoclosetschimeneapermite_mascotasgimnasioascensorconjunto_cerradopiscinasalon_comunalterrazavigilanciacoords_modifiedlocalidadbarrioestacion_tm_cercanadistancia_estacion_tm_mis_cerca_estacion_tmparque_cercanodistancia_parque_mis_cerca_parque
4300366d86ebfceda690e85512f5b4357929APARTAMENTOVENTA4.980000e+0884.003.02.0379000.01.0LA URIBE4.0ENTRE 10 Y 20 ANOS4.754657-74.037231CARRERA 17 #173-52apartamento en venta de 84m2, con vista interior, ubicado en un 16avo piso (torre 1 apto 1601), parqueadero propio cubierto (218). consta de 3 habitaciones, 2 banos, sala comedor, balcon (1m2) cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y zona social y piso ceramico en banos y cocina. el conjunto cuenta con salon comunal, gimnasio, parque infantil, zonas verdes y vigilancia privada las 24 horas. cerca a centro comercial 184 y centro comercial panama; cerca a almacenes de cadena como exito, carulla, tiendas d1 y ara; cerca a colegio calasanz y colegio eucaristico villa guadalupe; cerca a estacion de transmilenio portal norte y transmilenio calle 187; vias de acceso por la calle 170 y la carrera 15.habi.co2024-09-03 23:46:34.6652024-09-03 23:46:34.665https://habi.co/page-data/venta-apartamentos/19413080441/moraika---apartamento-venta-uribe-usaquen/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.01.00.01.00.00.00.00.0FalseUSAQUENCANAPROPortal Norte975.620PARQUE VECINAL URBANIZACIÓN CALLE 170 / ALAMEDA108.841
4300466d86ebfceda690e85512f5c4518221APARTAMENTOVENTA1.941300e+0847.002.01.0173999.00.0TIMIZA3.0ENTRE 5 Y 10 ANOS4.605351-74.149984CARRERA 72J # 42 35 SURapartamento en venta de 47m2, con vista exterior, ubicado en un 9degno piso (torre 1 apto 904), acceso por escaleras y ascensor. consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso laminado en habitaciones y zona social y ceramico en banos y cocina. el conjunto cuenta con vigilancia privada las 24 horas. cerca a centro comercial paseo villa del rio; cerca a almacenes de cadena como ara; cerca a colegio santo domingo bilingue; cerca a universidad del tolima; cerca a paraderos del sitp; vias de acceso por la carrera 72j y calle 42.habi.co2024-09-03 23:46:34.6932024-09-03 23:46:34.693https://habi.co/page-data/venta-apartamentos/21802649354/marconi---apartamento-venta-santa-catalina-kennedy/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseKENNEDYMORABIA IISevillana1175.500PARQUE VECINAL LA ALEJANDRA232.861
4300566d86ebfceda690e85512f5d2494474APARTAMENTOVENTA1.370000e+0847.003.01.062000.00.0TINTAL SUR2.0ENTRE 5 Y 10 ANOS4.628769-74.212831CALLE 84SUR # 96 - 85apartamento en venta de 47m2, con vista interior, ubicado en un 3er piso (torre 12 apto 304). consta de 2 habitaciones, 1 bano, sala comedor, cocina, estudio y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con salon social, parque infantil zonas verdes, parqueadero comunal y vigilancia privada las 24 horas. cerca a centros comerciales como prado verde y metrorecreo; cerca a almacenes de cadena como ara y d1; cerca a colegios como campo verde y parques de bogota; estaciones de transmilenio como portal americas; vias de acceso por la calle 84 sur y carrera 96.habi.co2024-09-03 23:46:34.7212024-09-03 23:46:34.721https://habi.co/page-data/venta-apartamentos/8918205840/aliso-parques-bogota---apartamento-venta-campo-verde-bosa/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseBOSAS.C. SAN BERNARDINO XXIIPortal Américas4408.600PARQUE VECINAL URBANIZACIÓN PORTAL DEL SOL905.810
4300666d86ebfceda690e85512f5e4274786APARTAMENTOVENTA1.210000e+0835.002.01.076200.00.0CALANDAIMA2.0ENTRE 10 Y 20 ANOS4.648645-74.171201CARRERA 97F # 34A SUR-30apartamento en venta de 35m2, con vista interior, ubicado en un 2do piso (torre 24 apto 202), acceso por escaleras. consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta cuenta con parque infantil, zonas verdes, salon social y vigilancia privada las 24 horas. cerca a centro comercial tintal plaza; cerca a almacenes de cadena comotienda d1, tienda ara, colsubsidio; cerca al colegio colegio codema ied; cerca a paraderos del sitp; vias de acceso por la calle 26 sur y carrera 97f.habi.co2024-09-03 23:46:35.4962024-09-03 23:46:35.497https://habi.co/page-data/venta-apartamentos/18309424453/tierra-buena---apartamento-venta-bogota-kennedy/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseKENNEDYLOS CONDOMINIOS DETIERRA BUENA IBiblioteca Tintal1776.060PARQUE VECINAL PRIMAVERA876.370
4300766d86ebfceda690e85512f5f4409301APARTAMENTOVENTA1.300000e+0837.002.01.077000.00.0EL PORVENIR2.0ENTRE 10 Y 20 ANOS4.641467-74.186482CALLE 52SUR # 97C - 20apartamento en venta de 37m2, con vista interior, ubicado en un 5to piso (torre 7 apto 504), acceso por escalera, parqueadero comunal. consta de 2 habitaciones, 1 bano, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en habitaciones, zona social, bano y cocina. el conjunto cancha de futbol, parque para ninos, bicicletero, salon comunal y vigilancia privada las 24 horas. cerca a centro comercial trebolis el porvenir; cerca a almacenes de cadena ara; cerca a colegio ciudadela educativa ; cerca a universidad distrital francisco jose de caldas sede bosa el porvenir; cerca a paraderos sitp; vias de acceso por la calle 52sur y carrera 97c.habi.co2024-09-03 23:46:36.4802024-09-03 23:46:36.480https://habi.co/page-data/venta-apartamentos/20198398992/reservado-3---apartamento-venta-reservado-bosa/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseBOSAS.C OSORIO DIEZPortal Américas2004.860PARQUE METROPOLITANO PORVENIR301.871
4300866d86ebfceda690e85512f604323513APARTAMENTOVENTA1.900000e+0849.003.02.0204624.00.0SUBA2.0ENTRE 10 Y 20 ANOS4.754530-74.080902CALLE 157A # 92 - 06apartamento en venta de 49m2, con vista interior, ubicado en un 7mo piso (torre 3 apto 720), acceso por ascensor. consta de 3 habitaciones, 2 banos, sala comedor, cocina y zona de lavanderia. tiene piso laminado en habitaciones ceramico en banos y cocina. el conjunto cuenta con salon comunal, parque infantil, parqueadero comunal y vigilancia privada las 24 horas. cerca a centro comercial centro suba; cerca a almacenes de cadena d1, ara, jumbo; cerca a colegio el salitre suba; cerca a estacion de transmilenio av. suba; vias de acceso por la carrera 92 y calle 157.habi.co2024-09-03 23:46:41.7282024-09-03 23:46:41.728https://habi.co/page-data/venta-apartamentos/18874824326/sol-plata---apartamento-venta-salitre-suba-suba/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.01.00.01.00.00.00.00.0FalseSUBAURB. OVIEDOPortal Suba1712.710PARQUE ZONAL CAMPO VERDE198.031
4300966d86ebfceda690e85512f614345085APARTAMENTOVENTA3.220000e+0866.003.02.0277400.01.0EL RINCON4.0ENTRE 10 Y 20 ANOS4.742568-74.092140CARRERA 100A # 141 - 10apartamento en venta de 61m2, con vista interior, ubicado en un 3er piso (torre h apto 302), acceso por ascensor, parqueadero propio cubierto (133). consta de 3 habitaciones, 2 banos, sala comedor, cocina integral y zona de lavanderia. tiene piso ceramico en todas sus zonas. el conjunto cuenta con zona social, salon comunal, guarderia infantil, zona de juegos y vigilancia privada las 24 horas. cerca a centro comercial plaza imperial; cerca a almacenes de cadena, tienda d1, jumbo, exito, olimpica; cerca a colegio filarmonico simon bolivar - sede a; cerca a estacion de transmilenio portal de suba; vias de acceso por la calle 141 y carrera 100a.habi.co2024-09-03 23:46:50.0322024-09-03 23:46:50.032https://habi.co/page-data/venta-apartamentos/19249352874/sua-1---apartamento-venta-flores-suba/page-data.jsonNaNNaNNaNNaN0.0NaNNaN0.00.00.00.01.00.00.00.00.0FalseSUBALOS TEJARES DE SUBA (PREDIO)La Campiña120.161PARQUE ZONAL BELLAVISTA DINDALITO631.250
4301066d86ebfceda690e85512f62MC5206960APARTAMENTOVENTA3.300000e+0890.003.02.0112000.00.0LA SOLEDAD4.0MAS DE 20 ANOS4.630547-74.079590NaN!oportunidad! apartamento de 90m2 con excelente ubicacion para remodelacion o inversion, cerca de universidades, park way, restaurantes, con facil acceso y transporte. el apartamento cuenta con una amplia sala comedor con vista exterior muy iluminado, una cocina tipo corredor, cuarto de servicio que conecta con el bano social, tres amplias habitaciones que comparten un bano en su corredor, adicionalmente, el apartamento cuenta con parqueaderos comunales, primer piso exterior sobre la carrera 30metrocuadrado.com2024-09-03 23:48:31.4092024-09-03 23:48:31.409NaNNaNUSADONaNNaN0.0NaNNaN0.00.00.00.00.00.00.00.00.0FalseTEUSAQUILLOLAS AMERICASAV. ElDorado33.211PARQUE METROPOLITANO EL RENACIMIENTO - PARQUE CEMENTERIO CENTRAL1211.620
4301166d86ebfceda690e85512f63MC5203688APARTAMENTOVENTA1.280000e+09157.003.03.01050000.03.0SANTA BARBARA6.0MAS DE 20 ANOS4.702636-74.027180NaNvendo hermoso apartamento en altos de bella suiza , con area de 157 metros, tercer piso, tres habitaciones, estudio, sala y comedor independiente, tres banos, calentador a gas, cocina integral con lavavajillas electrico y triturador de alimentos, estufa a gas, dos balcones/terrazas, tres parqueaderos, dos depositos : cancha de tenis, gimnasio, canchas de squash, salon social, parque de ninos, cancha de basketball, conjunto cerrado y vigilado. excelente ubicacion.metrocuadrado.com2024-09-03 23:48:43.9852024-09-03 23:48:43.985NaNNaNUSADONaNNaN0.0NaNNaN0.00.00.01.00.00.00.00.00.0FalseUSAQUENS.C. SAN NORTECalle 1273005.880PARQUE METROPOLITANO EL COUNTRY1269.560
4301266d86ebfceda690e85512f64MC4721977APARTAMENTOVENTA4.980000e+0884.343.02.0287000.01.0KENNEDY4.0ENTRE 10 Y 20 ANOS4.620531-74.130990NaNvista exterior piso 11, conjunto club house, piscina, tienda, cancha multiple, salon cafe, gimnasio, golfito, plazoletas, zonas juegos ninos, salones comunales, terraza bbq. puerta ppal de seguridad - rodeado de vias principales transmilenio americas, transmilenio av 68, boyaca, 1ero. mayo, cerca a c.c. plaza de las americas, parque mundo aventura, sao, home sentry, multiplex cine colombiametrocuadrado.com2024-09-03 23:49:02.0322024-09-03 23:49:02.032NaNNaNUSADONaNNaN0.0NaNNaN0.00.00.01.00.01.00.00.00.0FalseKENNEDYHIPOTECHO OCCIDENTAL IIMarsella1022.070PARQUE ZONAL LA IGUALDAD648.010